Combined classifier multi-signal modulation identification method based on fractional Fourier transform

A fractional Fourier and modulation recognition technology, which is applied in modulation type recognition, modulation carrier system, character and pattern recognition, etc., can solve the problems of slow local optimal iteration speed and affect the recognition effect, so as to improve the effectiveness of the system , Improve the recognition rate, improve the effect of classification accuracy

Inactive Publication Date: 2018-11-06
HARBIN ENG UNIV
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Problems solved by technology

Particle swarm optimization (PSO) can achieve better optimization results for SVM parameter optimization, but the PSO algorith

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  • Combined classifier multi-signal modulation identification method based on fractional Fourier transform
  • Combined classifier multi-signal modulation identification method based on fractional Fourier transform
  • Combined classifier multi-signal modulation identification method based on fractional Fourier transform

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Embodiment Construction

[0019] The following examples describe the present invention in more detail.

[0020] Step 1: Based on figure 2 The 9 types of radar signal parameter values ​​provided in Table 1 produce a radar signal set consisting of CW, LFM, BPSK, COSTAS, FRANK, P1, P2, P3 and P4. The analysis and processing of non-stationary signals such as radar signals cannot be limited to the time domain or frequency domain. Time-frequency analysis of radar signals is required, and SPWVD can smooth both time and frequency directions while effectively suppressing cross-terms. . Perform SPWVD on the first signal to obtain a time-frequency image, and the corresponding SPWVD formula is as follows:

[0021] The Wigner Ville distribution (WVD) can be obtained by Fourier transforming the time-varying local correlation function:

[0022]

[0023] Add the kernel function g(u)h(τ) to the Wigner Ville distribution to get SPWVD:

[0024]

[0025] In the formula, t is time, f and ω are frequency, and ω=2...

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Abstract

The invention provides a combined classifier multi-signal modulation identification method based on fractional Fourier transform. The method includes steps: firstly, solving the problem of multi-signal separation by employing an FRFT technology, then realizing accurate classification and identification of a strong signal with the combination of a SVM by employing a pre-trained CNN in time and frequency domains, extracting characteristics including the spectrum kurtosis, a root-mean-square value, an AR characteristic coefficient and a Renyi entropy etc. of a weak signal to realize characteristic fusion, performing dimension reduction on fusion characteristics by employing a principal component analysis (PCA) to improve the system validity, and inputting the characteristics after dimension reduction to the SVM to realize second signal classification and identification and finally realize accurate and rapid identification of the multi-signal modulation mode with low signal to noise ratio.

Description

technical field [0001] The invention relates to a joint classifier signal modulation identification method, in particular to a joint classifier signal modulation identification method based on fractional Fourier transform (FRFT) for separating multiple signals. Background technique [0002] As the electromagnetic environment becomes more and more dense, the problem of simultaneous arrival of multiple signals brings difficulties to the high reliability identification of signal modulation. Therefore, multi-signal separation has become a key problem to be solved before the classification and identification of single-signal modulation methods. [0003] The time-domain detection method used when multiple signals are not aliased in the time domain is based on the principle of the energy detection method. Although the time-domain algorithm has a small amount of calculation, simple implementation and fast detection speed, the outstanding problem is that it is sensitive to noise. It...

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Application Information

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IPC IPC(8): G06K9/62H04L27/00
CPCH04L27/0012G06F18/2411
Inventor 高敬鹏申良喜郜丽鹏蒋伊琳赵忠凯
Owner HARBIN ENG UNIV
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